Social media platforms like Twitter® or Facebook®, have influenced news gathering. Every minute, people around the world are posting pictures, videos, tweeting and otherwise communicating about all sorts of events and happenings. For example, a person may comment on what they see at a scene of an accident in real-time. Since people geographically close to an event are a valuable source of breaking news, the information generated by them is potentially very valuable. However, leveraging such information is very difficult.
According to statistics on the Twitter® website, there are approximately 320 million twitter users, of which, 65 million are in the United States and 254 million internationally (Twitter Q4 2015 Earnings Report, pp. 4). There are also approximately 350,000 tweets per minute. The percentage of valuable information is very small compared to the entire social media data available at a time. It has been noted that social media data primarily includes rumors, noise, spam, and mostly information useless to a professional consumer. As a result, potentially useful information is very hard to discover. Furthermore, discovery of useful information does not assure accuracy of the claimed event.
Currently, the tools in the marketplace take a bottom-up approach to tackling extraction of information from social media. Users interested in niche information may search by keywords or maintain broad databases of people to follow in hope to capture useful information from social media data. This bottom-up approach of information extraction requires guess work and constant maintenance of lists and keywords.
Accordingly, improved systems and techniques are needed that detect emerging trends at the social media data level and verify the authenticity of the emerging trends.